Online Dating Profile Verification Using Iterative Deception Detection
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Solution Overview
Problem
Existing online dating platforms lack comprehensive automated validation of user data, leading to potential fraud and safety risks due to falsified information, with users resorting to manual verification methods that are inefficient and incomplete.
Innovation Solution
A mobile app and web interface that utilizes iterative internet searches and machine learning algorithms to consolidate and analyze publicly available data, providing real-time, dynamic results and potential deception detection through pattern recognition and predictive modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual verification methods are used by users to check information about other users, then some level of verification can be achieved, but the process is inefficient and incomplete
Solution Approach 1:
The system performs automated verification of user information without requiring manual intervention from dating platform operators. The verification service independently searches multiple data sources, analyzes information consistency, and generates verification reports automatically, allowing the system to self-verify user profiles while maintaining high reliability and efficiency
Solution Approach 2:
An intermediary verification service is introduced between users and the dating platform. This service acts as a mediator that consolidates information from multiple sources (social media, public records, news databases) and presents verified information to users, resolving the contradiction by providing comprehensive verification without requiring users to manually check each source
2Reliability
If comprehensive validation of user data is implemented, then user safety and fraud prevention improve, but the complexity of the system increases
Solution Approach 1:
The verification system is segmented into distinct functional modules: information collection module that gathers data from multiple sources, analysis module that checks consistency across sources, and reporting module that presents findings. This segmentation allows comprehensive validation while managing complexity through modular design, where each module handles a specific aspect of the verification process
Solution Approach 2:
The verification service is designed as a universal system that can validate multiple types of user information (identity, employment, education, criminal records) through a single integrated platform. By consolidating various verification functions into one multi-functional service, the system achieves comprehensive validation without proportionally increasing complexity
3Loss of information
If users perform isolated internet searches to verify information, then some verification can be obtained, but the process is time-consuming and lacks comprehensiveness
Solution Approach 1:
The system merges multiple isolated search operations into a single consolidated verification process. It simultaneously queries multiple data sources (social media platforms, public records databases, news archives) and integrates the results, providing comprehensive information verification in one operation rather than requiring users to perform separate searches across different platforms
Solution Approach 2:
The verification service performs preliminary consolidation and analysis of information from multiple sources before presenting results to users. By pre-processing and organizing data from various sources in advance, the system reduces the time users would otherwise spend manually searching and cross-referencing information, while ensuring comprehensive coverage of all relevant data sources
Data Source
AI summary
A person (the user) interested in determining the authenticity of another person (the subject) enters the subject's name and other information into a mobile app or associated website. The subject information is then sent to a server program to automatically perform iterative searches for correlated online information. The server program also executes an analysis engine to perform unique heuristic and pattern matching analysis on the subject data and search results. The analysis engine is composed of a rules-based component, a machine learning pattern discovery component, and a machine learning pattern detection component. Search and analytics results are aggregated for presentation to the user in the app and website UI, organized in a categorical manner based on topic area. The result set consists of identified subject attributes that are potential areas of concern, subject attributes aligning to certain predictive trends, and subject information found on the internet.